Enabling Co-Innovation for a Successful Digital Transformation in Wind Energy Using a New Digital Ecosystem and a Fault Detection Case Study

Journal Article (2022)
Author(s)

S. Barber (Eastern Switzerland University of Applied Sciences)

Luiz Andre Moyses Lima (Voltalia)

Yoshiaki Sakagami (Federal Institute of Santa Catarina)

Julian Quick (University of Colorado - Boulder)

Effi Latiffianti (Institut Teknologi Sepuluh Nopember, Texas A&M University)

Y. Liu (Electric Power Research Institute (EPRI) Europe)

Riccardo M.G. Ferrari (TU Delft - Team Riccardo Ferrari)

Simon Letzgus (Technical University of Berlin)

Xujie Zhang (Zhejiang Sci-Tech University)

Florian Hammer (Eastern Switzerland University of Applied Sciences)

Research Group
Team Riccardo Ferrari
Copyright
© 2022 Sarah Barber, Luiz Andre Moyses Lima, Yoshiaki Sakagami, Julian Quick, Effi Latiffianti, Y. Liu, Riccardo M.G. Ferrari, Simon Letzgus, Xujie Zhang, Florian Hammer
DOI related publication
https://doi.org/10.3390/en15155638
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Sarah Barber, Luiz Andre Moyses Lima, Yoshiaki Sakagami, Julian Quick, Effi Latiffianti, Y. Liu, Riccardo M.G. Ferrari, Simon Letzgus, Xujie Zhang, Florian Hammer
Research Group
Team Riccardo Ferrari
Issue number
15
Volume number
15
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Abstract

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers.